Supervised Learning



E.g., 2016-10-24
E.g., 2016-10-24
E.g., 2016-10-24
Oct 24th 2016

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.

Average: 7.9 (8 votes)
Jul 20th 2016

This course introduces you to deep learning: the state-of-the-art approach to building artificial intelligence algorithms. We cover the basic components of deep learning, what it means, how it works, and develop code necessary to build various algorithms such as deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks.

Average: 10 (1 vote)
Jul 11th 2016

Learn the underlying principles required to develop scalable machine learning pipelines and gain hands-on experience using Apache Spark. Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability and optimization.

Average: 7.8 (4 votes)